"The virus mutates quite a bit," Deem said. "Normally, when we're doing rational drug design, we're trying to do it against a target protein that is not changing, and we're trying to find something that fits very nicely in the pocket of that protein.
"For the flu, these epitopes change. So rational drug design against one strain of the flu virus would only be useful for that one year, and then the virus would mutate the next year."
Deem wants to cut the amount of time between the analysis of flu strains and the manufacture of vaccines to fight them. He and graduate student Keyao Pan can predict the efficacy of H1N1 vaccines by estimating the antigenic "distance" -- the degree of difference between the epitopes -- for any two strains of virus.
Deem's technique assigns a numerical value to the antigenic distance between two strains. That tells researchers just how effective a virus might be. But it also offers a tipping point: If a value of zero is the perfect H1N1 vaccine, a value above roughly 0.4 indicates a vaccine that offers no protection at all.
That means there's a real incentive to formulating the vaccine as close to flu season as possible. It also means choosing strains of the virus that can be produced in high quantities but which are also as close as possible to the virus strain expected to hit. The current process is time-consuming: The novel H1N1 vaccines are incubating in hens' eggs -- the traditional method -- right now, and the United States expects to have 40 million doses in hand by mid-October, with 20 million doses arriving weekly thereafter, said Deem.
His calculations provide incentive to refine cell-bas
|Contact: David Ruth|